@article{Ngaorungsi202429,

title = {Operational Process Improvement for Outpatient Services at a Private Medium-Sized Hospital},

author = {Tanat Ngaorungsi and Parames Chutima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186882448&doi=10.4186%2fej.2024.28.2.29&partnerID=40&md5=08c5ba34eaeef2802350079b6c2af604},

doi = {10.4186/ej.2024.28.2.29},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Journal},

volume = {28},

number = {2},

pages = {29 – 65},

publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},

abstract = {This study is dedicated to enhancing the efficiency and efficacy of a medium-sized community hospital's services, which have limited space and experience a high volume of visits from diverse patient types with distinct service processes. The hospital challenges meeting the waiting time Key Performance Indicator (KPI) primarily from internal factors. The research methodology involves a comprehensive approach, encompassing the collection of qualitative and quantitative data, interviews with hospital staff, on-site observations, and a detailed examination of processing times at each step within the outpatient department. Upon data analysis, the study identifies and categorises key issues within the current Outpatient Department (OPD). These issues are encapsulated in three main categories, i.e., the unavailability of doctors during critical periods, insufficient staff for document delivery, and ineffective communication. Addressing the imperative of minimising patient system dwell time, a key competitive objective in the healthcare sector, this article is dedicated to identifying and implementing tools within a Lean framework. Tools such as root cause analysis, Poka-Yoke, and visual control are identified and implemented to optimise outpatient operations. Using simulation software, quantitative data is utilised to simulate and evaluate the outpatient process. The simulation results underscore significant periods during which doctors are absent, and an imbalance in workforce distribution emerges as a bottleneck. From a Lean perspective, recommendations are formulated to address these issues, emphasising the need for schedule balancing and minimising batch size through a proposed document method. The efficacy of these recommendations is subsequently validated using the simulation models. Through a series of optimisations and experiments, the average time in the system of social security patients has demonstrated a noteworthy reduction from 1,999 seconds to 1,820 seconds, reflecting an 8% improvement. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},

note = {Cited by: 4; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Singhakachen202499,

title = {Shortening the Cycle Time of the Fiber Ribbon Orientation Process for Wavelength Selective Switch Production using Design for Assembly and Disassembly Concepts},

author = {Piyanoot Singhakachen and Parames Chutima and Supachai Doungtongpol and Ronnachai Tantiwiwat},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204177091&doi=10.4186%2fej.2024.28.8.99&partnerID=40&md5=b0ace11c674769100e3390fedc4ae374},

doi = {10.4186/ej.2024.28.8.99},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Journal},

volume = {28},

number = {8},

pages = {99 – 116},

publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},

abstract = {The primary objective of the research was to shorten the cycle time of a particular process used in producing a new Wavelength Selective Switch (WSS) product by a multinational electronic manufacturing corporation. In recent years, the case study company has encountered difficulties with process cycle time exceeding predefined takt time when establishing a new production process for the freshly launched item. To identify areas for improvement, the study leveraged industrial engineering techniques, such as the Yamazumi Chart, line balancing (workload leveling) analysis, and method time measurement. After the production process data was thoroughly analysed, cycle time reduction opportunities emerged. After that, the jig design used in the present investigation was developed based on the highly effective and widely recognized mechanical engineering concepts of Design for Assembly (DFA) and Design for Disassembly (DFD). The aim was to confidently eliminate non-value-added processes in the fiber ribbon orientation step, resulting in increased efficiency and improved outcomes. The study reported a significant reduction of 87% in the cycle time required. The results also demonstrated that implementing certain methodologies could reduce the cycle time. In addition, this finding held significant importance for the industry, as it could lead to increased efficiency and productivity, ultimately leading to cost savings of 12% of its total production. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},

note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Arnonwattana202437,

title = {Leveraging Partner Country Factors in Deep Learning for Thailand’s Forecasted Inflation Accuracy Enhancement},

author = {Techin Arnonwattana and Parames Chutima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206901773&doi=10.4186%2fej.2024.28.6.37&partnerID=40&md5=1e8706d9dbe441cbd24dd6cf69f270f8},

doi = {10.4186/ej.2024.28.6.37},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Journal},

volume = {28},

number = {6},

pages = {37 – 58},

publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},

abstract = {This paper focuses on improving the accuracy of headline inflation forecasts in Thailand. By evaluating the performance of deep learning models, time series forecasting models, and hybrid models in 1-, 3-, 6-, and 12-month advance forecast periods are investigated. In addition, the efficacy of including partner countries' inflation variables in the model is evaluated. There is a comparative analysis of various models, including ANN, RNN, LSTM, VAR, the hybrid model (VAR-ANN), and the BOTMM benchmark model of the Bank of Thailand. This study aimed to identify the most efficient model and demonstrate the impact of including partner countries' inflation on forecast accuracy. The results reveal that the hybrid model (VAR-ANN) consistently outperforms other models over several forecast periods, showing its superiority in capturing inflation trends. Specifically, the hybrid model (VAR-ANN) shows an average RMSE improvement of 50.36% over the BOTMM benchmark model from 2020 to 2022, with performance improvements of 52.94% in 2020, 56.56% in 2021, and 47.25% in 2022. In addition, the inclusion of partner countries' inflation significantly increases the accuracy of the predictions. These results are helpful for policymakers and practitioners working on inflation forecasts and emphasize the practical advantages of the hybrid model for enhancing prediction accuracy for Thailand's economic indicators. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},

note = {Cited by: 3; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Amapat202479,

title = {High-Density Polyethylene Film Price Forecast in Southeast Asia Market with Deep Learning},

author = {Narongchon Amapat and Parames Chutima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214918988&doi=10.4186%2fej.2024.28.12.79&partnerID=40&md5=f0cd7f0dd850424c8edca4e165c15e65},

doi = {10.4186/ej.2024.28.12.79},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Journal},

volume = {28},

number = {12},

pages = {79 – 100},

publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},

abstract = {Neural networks (NN) have been used for over a decade to predict time series data, with various algorithms including linear and non-linear models. Forecasting and assessing polymer market prices are crucial for plastic resin producers due to the complexity and uncertainty of resource availability. This encompasses feedstock planning, raw material procurement, technological advancements for product transitions, sales planning, pricing strategies for commercialization, and investments driven by macroeconomic factors. Previous literature primarily utilized numerical data as input for deep learning models. This research contended that structured data by itself was inadequate for models to precisely predict outcomes in the volatile, uncertain, complex, and ambiguous (VUCA) environment. Three deep learning architectures, Long-Short Term Memory (LSTM), Encoder-Decoder, Temporal Convolutional Network and Recurrent Neuron Network (TCNRNN), were reviewed in this research to determine the most effective architecture for analysing structured data. Additionally, Natural Language Processing (NLP) was implemented in this research to gather market sentiment and enhance forecast accuracy. The study utilizes commodity market price announcements, economic indicators, and insight reports from reputable publishers. The study utilizes commodity market prices, economic indicators, and insightful reports. All information was obtained from a reputable publisher. The results were compared with the legacy model, which involved a human analyst and a linear regression model. Model performance was assessed using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE), and ANOVA. The linear regression forecast together with the human analyst model has an acceptable accuracy with a MAPE of 45.1%. Neural networks containing sentiment analyzers have been found to surpass the performance of human analysts and a linear regression model, with a MAPE of 17.1%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},

note = {Cited by: 1; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chaowai202421,

title = {Demand Forecasting and Ordering Policy of Fast-Moving Consumer Goods with Promotional Sales in a Small Trading Firm},

author = {Kanjanataj Chaowai and Parames Chutima},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193916907&doi=10.4186%2fej.2024.28.4.21&partnerID=40&md5=155659b5c4843b01f4291ab9c90eb98b},

doi = {10.4186/ej.2024.28.4.21},

year  = {2024},

date = {2024-01-01},

journal = {Engineering Journal},

volume = {28},

number = {4},

pages = {21 – 40},

publisher = {Chulalongkorn University, Faculty of Fine and Applied Arts},

abstract = {This research focuses on enhancing inventory management for fast-moving consumer goods (FMCGs) with promotional sales in a small trading company, particularly high-end items with fluctuating demand patterns. The analysis revealed that promotional campaigns led to an average demand increase of 60.44% for WM 85ML, and 161.76% for SW 85ML, highlighting the importance of including these variables in demand forecasting models. The research aims to determine an effective forecasting method for the company and develop an improved purchasing strategy. The methodology encompasses a comprehensive review of the existing system, problem investigation, solution proposal, and result analysis. Quantitative time-series forecasting methodologies specifically tailored to such luxury FMCGs were introduced including Exponential Smoothing and Holt-Winters’s Additive and Multiplicative forecasting. The application of these methods has led to a significant enhancement in forecast accuracy, with an approximate 90% improvement. The research's pivotal contribution is the development of a hybrid order policy named “Periodic Review with Safety Stocks and Reorder Point,” which merges a fixed-order quantity model with a fixed-time period model. This hybrid approach has practical implications for maintaining efficient inventory levels, enabling continuous promotional activities, and potentially reducing the company's inventory costs by approximately 30%. © 2024, Chulalongkorn University, Faculty of Fine and Applied Arts. All rights reserved.},

note = {Cited by: 4; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}